The subject matter described herein relates to systems and methods for processing optical images, and, more particularly, to automatic detection of polyps in optical images.
Colorectal cancer (CRC) is the second highest cause of cancer-related deaths in the United States with 50,830 estimated deaths in 2013. More than 80% of CRC cases arise from adenomatous polyps, which are precancerous abnormal growths of the colon wall. The preferred screening method for polyp detection and removal is an optical colonoscopy (OC) procedure, during which a colonoscopist meticulously examines the colon wall using a tiny camera that is inserted and guided through the colon. The goal of an OC is to detect and remove colorectal polyps, which may be precursors to CRC. Thus, it has been shown that timely removal of polyps can significantly reduce the mortality of CRC.
However, polyp detection with OC remains a challenging task and, as evidenced by several clinical studies, a significant portion of flat and pedunculated polyps remain undetected during colon screening with OC. High polyp detection rate requires a high level of attentiveness, alertness, and sensitivity to visual characteristics of polyps from colonoscopists and such qualities may only be procured after years of practice and experience. It is therefore important to reduce polyp miss-rate as it decreases the incidence and mortality of CRC.
Computer-aided polyp detection has recently been considered as a tool for reducing polyp miss-rate. For example, during an OC procedure, regions with suspected polyps can be highlighted for further examination. Existing approaches for polyp detection primarily rely on the shape or texture of polyps. However, shape information is susceptible to partial and fragmented image segmentation and can mislead a detector towards irrelevant objects in the complex endoluminal scene. Texture may also be unreliable because its visibility depends on camera-polyp distance. Thus, the texture of a polyp becomes fully visible only when the camera captures close shots of the surface of a polyp. This condition is often met when polyps have already been detected by operators. On the other hand, shape information cannot be considered as a reliable measure because polyps appear in a variety of forms ranging from sessile to peduncular shapes. Therefore, texture-based and shape-based polyp detectors offer limited practical value.
Consequently, considering such limitations of previous technological approaches, it would be desirable to have a system and method for accurate and reliable polyp detection in optical colonoscopy images that is shape-based and can compensate for the concomitant drawbacks of shape-based detection.